WL2_climate_distance
Brandie Quarles
2025-01-17
Climate Distance at the WL2 Garden
Relevant Libraries and Functions
library(raster)## Loading required package: splibrary(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ tidyr::extract() masks raster::extract()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::select() masks raster::select()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(conflicted)
conflicts_prefer(dplyr::select())## [conflicted] Will prefer dplyr::select over any other package.conflicts_prefer(dplyr::filter)## [conflicted] Will prefer dplyr::filter over any other package.library(cowplot)
library(QBMS) #for function calc_biovars to calculate bioclim variables
library(boot)
library(broom)
sem <- function(x, na.rm=FALSE) {
sd(x,na.rm=na.rm)/sqrt(length(na.omit(x)))
} #standard error function
get_legend<-function(myggplot){
tmp <- ggplot_gtable(ggplot_build(myggplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend)
} #legend function for grid_arrange
elev_three_palette <- c("#0043F0", "#C9727F", "#F5A540") #colors from Gremer et al 2019
elev_order <- c("High", "Mid", "Low")WL2 Climate Data Nov 2022 - Oct 2023 (Plants only grown July 2023-Oct 2023)
From Flint (changed this from Magney MetStation)
WL2_climate <- read_csv("../output/Climate/flint_climate_UCDpops.csv") %>%
filter(parent.pop=="WL2_Garden") %>%
filter(year>=2022, year<2024) %>%
filter(if_else(year==2022, month=="nov" | month=="dec",
month!="nov" & month !="dec")) %>%
mutate(tavg = (tmn + tmx)/2, t_diurnal = (tmx-tmn))## Rows: 38675 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): parent.pop, elevation.group, month
## dbl (11): elev_m, Lat, Long, year, aet, cwd, pck, pet, ppt, tmn, tmx
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(WL2_climate)## # A tibble: 6 × 16
## parent.pop elevation.group elev_m Lat Long year month aet cwd pck
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 WL2_Garden High 2020 38.8 -120. 2022 dec 4 20 610.
## 2 WL2_Garden High 2020 38.8 -120. 2022 nov 2.48 29.1 40.4
## 3 WL2_Garden High 2020 38.8 -120. 2023 apr 41.5 46.4 1787.
## 4 WL2_Garden High 2020 38.8 -120. 2023 aug 106. 43.8 0
## 5 WL2_Garden High 2020 38.8 -120. 2023 feb 8.62 22.4 1422.
## 6 WL2_Garden High 2020 38.8 -120. 2023 jan 7.23 16.4 1208.
## # ℹ 6 more variables: pet <dbl>, ppt <dbl>, tmn <dbl>, tmx <dbl>, tavg <dbl>,
## # t_diurnal <dbl>summary(WL2_climate)## parent.pop elevation.group elev_m Lat
## Length:12 Length:12 Min. :2020 Min. :38.83
## Class :character Class :character 1st Qu.:2020 1st Qu.:38.83
## Mode :character Mode :character Median :2020 Median :38.83
## Mean :2020 Mean :38.83
## 3rd Qu.:2020 3rd Qu.:38.83
## Max. :2020 Max. :38.83
## Long year month aet
## Min. :-120.3 Min. :2022 Length:12 Min. : 2.48
## 1st Qu.:-120.3 1st Qu.:2023 Class :character 1st Qu.: 6.98
## Median :-120.3 Median :2023 Mode :character Median : 37.69
## Mean :-120.3 Mean :2023 Mean : 44.94
## 3rd Qu.:-120.3 3rd Qu.:2023 3rd Qu.: 78.27
## Max. :-120.3 Max. :2023 Max. :115.16
## cwd pck pet ppt
## Min. : 14.83 Min. : 0.0 Min. : 23.60 Min. : 0.01
## 1st Qu.: 21.79 1st Qu.: 0.0 1st Qu.: 31.45 1st Qu.: 36.49
## Median : 38.83 Median : 589.6 Median : 81.55 Median : 68.33
## Mean : 41.58 Mean : 742.9 Mean : 86.52 Mean :207.19
## 3rd Qu.: 49.56 3rd Qu.:1323.9 3rd Qu.:134.12 3rd Qu.:319.23
## Max. :100.37 Max. :1986.3 Max. :176.10 Max. :641.41
## tmn tmx tavg t_diurnal
## Min. :-6.150 Min. : 2.130 Min. :-2.0100 Min. : 8.25
## 1st Qu.:-4.258 1st Qu.: 4.165 1st Qu.:-0.1288 1st Qu.: 9.42
## Median : 0.870 Median :11.870 Median : 6.3700 Median :10.30
## Mean : 1.762 Mean :12.081 Mean : 6.9212 Mean :10.32
## 3rd Qu.: 6.145 3rd Qu.:17.520 3rd Qu.:11.8325 3rd Qu.:11.32
## Max. :13.220 Max. :25.950 Max. :19.5850 Max. :12.73WL2_climate_flint <- WL2_climate %>% #get the means for the variables to compare to home sites
summarise(cwd_WL2=mean(cwd),ppt_WL2=mean(ppt), pck_WL2=mean(pck), tmn_WL2=mean(tmn), tmx_WL2=mean(tmx))
WL2_climate_flint## # A tibble: 1 × 5
## cwd_WL2 ppt_WL2 pck_WL2 tmn_WL2 tmx_WL2
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 41.6 207. 743. 1.76 12.1WL2 Climate Trends
WL2_climate$month <- factor(WL2_climate$month, levels = c("nov","dec","jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct"))
WL2_climate %>%
ggplot(aes(x=month,y=cwd)) +
geom_point()
WL2_climate %>%
ggplot(aes(x=month,y=pck)) +
geom_point() 
WL2_climate %>%
ggplot(aes(x=month,y=tmx)) +
geom_point()
WL2_climate %>%
ggplot(aes(x=month,y=tmn)) +
geom_point()
WL2_climate %>%
ggplot(aes(x=month,y=ppt)) +
geom_point()
BioClim
Prep
wl2_bioclim_allyear_prep <- WL2_climate %>%
mutate(wtr_year="2023") %>%
rename(tmin=tmn, tmax=tmx, year_cal=year, year=wtr_year) %>% #rename columns to match what calc_biovars expects
arrange(parent.pop, year, month)Calculation
wl2_bioclim_all_year <- wl2_bioclim_allyear_prep %>% calc_biovars() SUBSET
wl2_bioclim_all_year_final <- wl2_bioclim_all_year %>%
select(ann_tmean_WL2=bio1, mean_diurnal_range_WL2=bio2,
temp_seasonality_WL2=bio4, temp_ann_range_WL2=bio7, tmean_wettest_quarter_WL2=bio8,
tmean_driest_quarter_WL2=bio9, ann_ppt_WL2=bio12, ppt_seasonality_WL2=bio15,
ppt_warmest_quarter_WL2=bio18, ppt_coldest_quarter_WL2=bio19) %>%
mutate_if(is.character, as.numeric)
wl2_bioclim_all_year_final## ann_tmean_WL2 mean_diurnal_range_WL2 temp_seasonality_WL2 temp_ann_range_WL2
## 1 6.92125 10.31917 758.8279 32.1
## tmean_wettest_quarter_WL2 tmean_driest_quarter_WL2 ann_ppt_WL2
## 1 -0.555 16.93833 2486.31
## ppt_seasonality_WL2 ppt_warmest_quarter_WL2 ppt_coldest_quarter_WL2
## 1 119.8368 88.98 1413.4Gower’s Climate Distance
(1/P) * SUM ((absolute value(Ai - Bi)) / range(i)) for each variable
P = number of environmental variables = 13 (without CWD)
Ai = 30 year avg of that variable for the home site
Bi = July 2023-Dec 2023 avg of that variable for the WL2 garden
Range(i) = maximum - minimum of that variable in the whole data set (across sites)
Prep WL2 data for range calculations
WL2_climate_all <- bind_cols(WL2_climate_flint, wl2_bioclim_all_year_final)
WL2_range_prep <- WL2_climate_all %>%
mutate(parent.pop="WL2_Garden") %>%
rename_with(~str_remove(., "_WL2"), everything())Bootstrapping
Load data with all 30 years
recent_clim_boot <- read_csv("../output/Climate/fullyear_wtr_year_avgs_Recent.csv")## Rows: 690 Columns: 21
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): parent.pop, elevation.group
## dbl (19): elev_m, Lat, Long, year, cwd, pck, ppt, tmn, tmx, ann_tmean, mean_...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.recent_clim_boot_nest <- recent_clim_boot %>% nest(.by=year) #nest to prepare for bootstrapping
recent_clim_boot_nest## # A tibble: 30 × 2
## year data
## <dbl> <list>
## 1 1994 <tibble [23 × 20]>
## 2 1995 <tibble [23 × 20]>
## 3 1996 <tibble [23 × 20]>
## 4 1997 <tibble [23 × 20]>
## 5 1998 <tibble [23 × 20]>
## 6 1999 <tibble [23 × 20]>
## 7 2000 <tibble [23 × 20]>
## 8 2001 <tibble [23 × 20]>
## 9 2002 <tibble [23 × 20]>
## 10 2003 <tibble [23 × 20]>
## # ℹ 20 more rowshistorical_clim_boot <- read_csv("../output/Climate/fullyear_wtr_year_avgs_Historical.csv")## Rows: 690 Columns: 21
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): parent.pop, elevation.group
## dbl (19): elev_m, Lat, Long, year, cwd, pck, ppt, tmn, tmx, ann_tmean, mean_...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.historical_clim_boot_nest <- historical_clim_boot %>% nest(.by=year)
historical_clim_boot_nest## # A tibble: 30 × 2
## year data
## <dbl> <list>
## 1 1964 <tibble [23 × 20]>
## 2 1965 <tibble [23 × 20]>
## 3 1966 <tibble [23 × 20]>
## 4 1967 <tibble [23 × 20]>
## 5 1968 <tibble [23 × 20]>
## 6 1969 <tibble [23 × 20]>
## 7 1970 <tibble [23 × 20]>
## 8 1971 <tibble [23 × 20]>
## 9 1972 <tibble [23 × 20]>
## 10 1973 <tibble [23 × 20]>
## # ℹ 20 more rowsCreate the gower_calc function
#data <- recent_clim_boot_nest
#P=14
gowers_calc <- function(data, indices, P) { #function with all of the code necessary for calculating gowers distance
#data = _clim_boot (recent or historical) - needs to be nested by year; P = # climate variables
#need to make WL2_range_prep before running this function
data <-data[indices,] # subset per bootstrap indices
data <- data %>% unnest(data) #unnest so the function can access the climate data
data_means <- data %>%
group_by(parent.pop, elevation.group, elev_m, Lat, Long) %>%
summarise_at(c("cwd", "pck", "ppt", "tmn", "tmx", "ann_tmean", "mean_diurnal_range",
"temp_seasonality", "temp_ann_range",
"tmean_wettest_quarter", "tmean_driest_quarter", "ann_ppt",
"ppt_seasonality","ppt_warmest_quarter", "ppt_coldest_quarter"),
c(mean), na.rm = TRUE) #get 30 year averages for each climate variable
range_merge <- bind_rows(data_means, WL2_range_prep)
WL2_home_climate_ranges <- range_merge %>% #calculate ranges
ungroup() %>%
summarise(cwd_range=max(cwd)-min(cwd),
pck_range=max(pck)-min(pck),
ppt_range=max(ppt)-min(ppt),
tmn_range=max(tmn)-min(tmn),
tmx_range=max(tmx)-min(tmx),
ann_tmean_range=max(ann_tmean)-min(ann_tmean),
mean_diurnal_range_range=max(mean_diurnal_range)-min(mean_diurnal_range),
temp_seasonality_range=max(temp_seasonality)-min(temp_seasonality),
temp_ann_range_range=max(temp_ann_range)-min(temp_ann_range),
tmean_wettest_quarter_range=max(tmean_wettest_quarter)-min(tmean_wettest_quarter),
tmean_driest_quarter_range=max(tmean_driest_quarter)-min(tmean_driest_quarter),
ann_ppt_range=max(ann_ppt)-min(ann_ppt),
ppt_seasonality_range=max(ppt_seasonality)-min(ppt_seasonality),
ppt_warmest_quarter_range=max(ppt_warmest_quarter)-min(ppt_warmest_quarter),
ppt_coldest_quarter_range=max(ppt_coldest_quarter)-min(ppt_coldest_quarter))
WL2_home_climate <- bind_cols(WL2_climate_all, data_means) #add WL2 climate data to home climate data
WL2_home_climate_with_ranges <- bind_cols(WL2_home_climate, WL2_home_climate_ranges) #add in ranges
gowers_calc_each_var <- WL2_home_climate_with_ranges %>% #variable by variable calc
mutate(cwd_gowers=abs(cwd_WL2-cwd) / cwd_range,
pck_gowers=abs(pck_WL2-pck) / pck_range,
ppt_gowers=abs(ppt_WL2 - ppt) / ppt_range,
tmn_gowers=abs(tmn_WL2 - tmn) / tmn_range,
tmx_gowers=abs(tmx_WL2 - tmx) / tmx_range,
ann_tmean_gowers=abs(ann_tmean_WL2 - ann_tmean) / ann_tmean_range,
mean_diurnal_range_gowers=abs(mean_diurnal_range_WL2 - mean_diurnal_range) / mean_diurnal_range_range,
temp_seasonality_gowers=abs(temp_seasonality_WL2 - temp_seasonality) / temp_seasonality_range,
temp_ann_range_gowers=abs(temp_ann_range_WL2 - temp_ann_range) / temp_ann_range_range,
tmean_wettest_quarter_gowers=abs(tmean_wettest_quarter_WL2 - tmean_wettest_quarter) / tmean_wettest_quarter_range,
tmean_driest_quarter_gowers=abs(tmean_driest_quarter_WL2 - tmean_driest_quarter) / tmean_driest_quarter_range,
ann_ppt_gowers=abs(ann_ppt_WL2 - ann_ppt) / ann_ppt_range,
ppt_seasonality_gowers=abs(ppt_seasonality_WL2 - ppt_seasonality) / ppt_seasonality_range,
ppt_warmest_quarter_gowers=abs(ppt_warmest_quarter_WL2 - ppt_warmest_quarter) / ppt_warmest_quarter_range,
ppt_coldest_quarter_gowers=abs(ppt_coldest_quarter_WL2 - ppt_coldest_quarter) / ppt_coldest_quarter_range) %>%
dplyr::select(parent.pop, elevation.group, elev_m, ends_with("_gowers"))
gowers_calc_per_pop <- gowers_calc_each_var %>% #final gowers calc
mutate(Gowers_Dist=(1/P)*(cwd_gowers + pck_gowers + ppt_gowers + tmn_gowers + tmx_gowers +
ann_tmean_gowers + mean_diurnal_range_gowers +
temp_seasonality_gowers +temp_ann_range_gowers +
tmean_wettest_quarter_gowers +
tmean_driest_quarter_gowers +ann_ppt_gowers +
ppt_seasonality_gowers + ppt_warmest_quarter_gowers +
ppt_coldest_quarter_gowers)) %>%
dplyr::select(parent.pop, elevation.group, elev_m, Gowers_Dist)
gowers_calc_per_pop %>% pull(Gowers_Dist) #make the result a vector
}
#gowers_calc(recent_clim_boot_nest, P=15) #the function worksPerform the bootstrap sampling
Recent
gowers.boot_recent <- boot(data=recent_clim_boot_nest, statistic=gowers_calc, R=1000, P=15) #will sample each row (year) with replacement
gowers.boot_recent$t0 #looks correct ## [1] 0.7141616 0.6056544 0.3865863 0.3640923 0.4675426 0.5593677 0.5512696
## [8] 0.3778205 0.3731319 0.3850069 0.6405278 0.4548428 0.4233460 0.3980990
## [15] 0.5715199 0.4387455 0.3959884 0.4181980 0.4807671 0.4605416 0.4110137
## [22] 0.3922302 0.4107557#str(gowers.boot_recent)
for(i in 1:23) {
plot(gowers.boot_recent, index=i) #distributions look normal for the most part
}
#boot.ci(gowers.boot_recent, type="norm", index = 1) # for the first pop
boot_recent_results <- tidy(gowers.boot_recent,conf.int=TRUE,conf.method="norm") %>% #all pops
rename(Gowers_Dist = statistic) %>%
mutate(TimePd="Recent")Historical
gowers.boot_historical <- boot(data=historical_clim_boot_nest, statistic=gowers_calc, R=1000, P=15) #will sample each row (year) with replacement
gowers.boot_historical$t0 #looks correct ## [1] 0.6473010 0.5694875 0.4367699 0.4232769 0.4574498 0.5537173 0.5297012
## [8] 0.4628629 0.4658955 0.4653493 0.6335281 0.4493608 0.4209900 0.4149814
## [15] 0.5402588 0.4549991 0.4251657 0.4074671 0.4929387 0.5557644 0.4127448
## [22] 0.4235359 0.4547568#str(gowers.boot_historical)
for(i in 1:23) {
plot(gowers.boot_historical, index=i) #distributions look normal for the most part
}
#boot.ci(gowers.boot_historical, type="norm", index = 1) # for the first pop
boot_historical_results <- tidy(gowers.boot_historical,conf.int=TRUE,conf.method="norm") %>% #all pops
rename(Gowers_Dist = statistic) %>%
mutate(TimePd="Historical")Plot the results
pops <- recent_clim_boot %>% select(parent.pop, elevation.group, elev_m, Lat, Long) %>% unique()
boot_gowers_recent_pops <- bind_cols(pops, boot_recent_results) %>% arrange(Gowers_Dist)
boot_gowers_historical_pops <- bind_cols(pops, boot_historical_results) %>% arrange(Gowers_Dist)
boot_gowers_results_all <- bind_rows(boot_gowers_recent_pops, boot_gowers_historical_pops)
write_csv(boot_gowers_results_all, "../output/Climate/full_year_GowersEnvtalDist_WL2_wtr_year.csv")
recent_fig <- boot_gowers_results_all %>%
filter(TimePd=="Recent") %>%
ggplot(aes(x=fct_reorder(parent.pop, Gowers_Dist), y=Gowers_Dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=conf.low, ymax=conf.high),
width=.1, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(y="Gowers Envtal Distance \n from WL2", fill="Elevation (m)", x="Population", title = "Recent Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
historical_fig <- boot_gowers_results_all %>%
filter(TimePd=="Historical") %>%
ggplot(aes(x=fct_reorder(parent.pop, Gowers_Dist), y=Gowers_Dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=conf.low, ymax=conf.high),
width=.1, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(y="Gowers Envtal Distance \n from WL2", fill="Elevation (m)", x="Population", title="Historic Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
plot_grid(historical_fig, recent_fig)
ggsave("../output/Climate/full_year_Gowers_fromWL2_wtr_year.png", width = 24, height = 8, units = "in")Flint Climate Distance
Gowers
#need to modify the gowers_calc function to only include Flint variables
gowers_calc_flint <- function(data, indices, P) { #function with all of the code necessary for calculating gowers distance
#data = _clim_boot (recent or historical) - needs to be nested by year; P = # climate variables
#need to make WL2_range_prep before running this function
data <-data[indices,] # subset per bootstrap indices
data <- data %>% unnest(data) #unnest so the function can access the climate data
data_means <- data %>%
group_by(parent.pop, elevation.group, elev_m, Lat, Long) %>%
summarise_at(c("cwd", "pck", "ppt", "tmn", "tmx"),
c(mean), na.rm = TRUE) #get 30 year averages for each climate variable
range_merge <- bind_rows(data_means, WL2_range_prep)
WL2_home_climate_ranges <- range_merge %>% #calculate ranges
ungroup() %>%
summarise(cwd_range=max(cwd)-min(cwd),
pck_range=max(pck)-min(pck),
ppt_range=max(ppt)-min(ppt),
tmn_range=max(tmn)-min(tmn),
tmx_range=max(tmx)-min(tmx))
WL2_home_climate <- bind_cols(WL2_climate_all, data_means) #add WL2 climate data to home climate data
WL2_home_climate_with_ranges <- bind_cols(WL2_home_climate, WL2_home_climate_ranges) #add in ranges
gowers_calc_each_var <- WL2_home_climate_with_ranges %>% #variable by variable calc
mutate(cwd_gowers=abs(cwd_WL2-cwd) / cwd_range,
pck_gowers=abs(pck_WL2-pck) / pck_range,
ppt_gowers=abs(ppt_WL2 - ppt) / ppt_range,
tmn_gowers=abs(tmn_WL2 - tmn) / tmn_range,
tmx_gowers=abs(tmx_WL2 - tmx) / tmx_range)
gowers_calc_per_pop <- gowers_calc_each_var %>% #final gowers calc
mutate(Gowers_Dist=(1/P)*(cwd_gowers + pck_gowers + ppt_gowers + tmn_gowers + tmx_gowers)) %>%
dplyr::select(parent.pop, elevation.group, elev_m, Gowers_Dist)
gowers_calc_per_pop %>% pull(Gowers_Dist) #make the result a vector
}Recent
recent_clim_boot_flint_nest <- recent_clim_boot %>% select(parent.pop:tmx) %>% nest(.by=year)
gowers.boot_flint_recent <- boot(data=recent_clim_boot_flint_nest, statistic=gowers_calc_flint, R=1000, P=5) #will sample each row (year) with replacement
gowers.boot_flint_recent$t0 #looks correct ## [1] 0.8298694 0.7318034 0.3851232 0.3155863 0.5852761 0.6754010 0.6438531
## [8] 0.2547820 0.2282119 0.2594628 0.7742664 0.5666877 0.4834382 0.4266042
## [15] 0.6913891 0.4941955 0.3850193 0.4891084 0.5246936 0.4792476 0.4161825
## [22] 0.3683864 0.4286654#str(gowers.boot_flint_recent)
for(i in 1:23) {
plot(gowers.boot_flint_recent, index=i) #distributions look normal for the most part
}
#boot.ci(gowers.boot_flint_recent, type="norm", index = 1) # for the first pop
boot_flint_recent_results <- tidy(gowers.boot_flint_recent,conf.int=TRUE,conf.method="norm") %>% #all pops
rename(Gowers_Dist = statistic) %>%
mutate(TimePd="Recent")Historic
historical_clim_boot_flint_nest <- historical_clim_boot %>% select(parent.pop:tmx) %>% nest(.by=year)
gowers.boot_flint_historical <- boot(data=historical_clim_boot_flint_nest, statistic=gowers_calc_flint, R=1000, P=5) #will sample each row (year) with replacement
gowers.boot_flint_historical$t0 #looks correct ## [1] 0.7887628 0.7017723 0.3657746 0.3031255 0.5469091 0.6465280 0.6197956
## [8] 0.2599242 0.2593862 0.2634549 0.7622744 0.4854304 0.4103923 0.3843689
## [15] 0.6480619 0.4538663 0.3528659 0.4302055 0.4873648 0.5059971 0.3817998
## [22] 0.3579058 0.4310411#str(gowers.boot_flint_historical)
for(i in 1:23) {
plot(gowers.boot_flint_historical, index=i) #distributions look normal for the most part
}
#boot.ci(gowers.boot_flint_historical, type="norm", index = 1) # for the first pop
boot_flint_historical_results <- tidy(gowers.boot_flint_historical,conf.int=TRUE,conf.method="norm") %>% #all pops
rename(Gowers_Dist = statistic) %>%
mutate(TimePd="Historical")Plot the results
boot_flint_gowers_recent_pops <- bind_cols(pops, boot_flint_recent_results) %>% arrange(Gowers_Dist)
boot_flint_gowers_historical_pops <- bind_cols(pops, boot_flint_historical_results) %>% arrange(Gowers_Dist)
boot_flint_gowers_results_all <- bind_rows(boot_flint_gowers_recent_pops, boot_flint_gowers_historical_pops)
write_csv(boot_flint_gowers_results_all, "../output/Climate/full_year_GowersEnvtalDist_WL2Flint_wtr_year.csv")
recent_fig <- boot_flint_gowers_results_all %>%
filter(TimePd=="Recent") %>%
ggplot(aes(x=fct_reorder(parent.pop, Gowers_Dist), y=Gowers_Dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=conf.low, ymax=conf.high),
width=.1, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(y="Gowers Envtal Distance \n from WL2", fill="Elevation (m)", x="Population", title = "Recent Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
historical_fig <- boot_flint_gowers_results_all %>%
filter(TimePd=="Historical") %>%
ggplot(aes(x=fct_reorder(parent.pop, Gowers_Dist), y=Gowers_Dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=conf.low, ymax=conf.high),
width=.1, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(y="Gowers Envtal Distance \n from WL2", fill="Elevation (m)", x="Population", title="Historic Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
plot_grid(historical_fig, recent_fig)
ggsave("../output/Climate/full_year_Gowers_Flint_fromWL2_wtr_year.png", width = 24, height = 8, units = "in")Subtraction
pops_flint_avgs <- read_csv("../output/Climate/fullyear_FlintAvgs_wtr_year.csv")## Rows: 46 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): parent.pop, elevation.group, TimePd
## dbl (8): elev_m, Lat, Long, cwd, pck, ppt, tmn, tmx
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.pops_flint_recent_avgs <- pops_flint_avgs %>% filter(TimePd=="Recent")
recent_flint_dist_prep <- bind_cols(WL2_climate_flint, pops_flint_recent_avgs)
names(recent_flint_dist_prep)## [1] "cwd_WL2" "ppt_WL2" "pck_WL2" "tmn_WL2"
## [5] "tmx_WL2" "parent.pop" "elevation.group" "elev_m"
## [9] "Lat" "Long" "cwd" "pck"
## [13] "ppt" "tmn" "tmx" "TimePd"recent_flint_dist <- recent_flint_dist_prep %>%
mutate(ppt_dist=ppt_WL2 - ppt,
cwd_dist=cwd_WL2 - cwd,
pck_dist=pck_WL2 - pck,
tmn_dist=tmn_WL2 - tmn,
tmx_dist=tmx_WL2 - tmx) %>%
dplyr::select(parent.pop, elevation.group, elev_m, ends_with("_dist"))
pops_flint_historic_avgs <- pops_flint_avgs %>% filter(TimePd=="Historical")
historic_flint_dist_prep <- bind_cols(WL2_climate_flint, pops_flint_historic_avgs)
names(historic_flint_dist_prep)## [1] "cwd_WL2" "ppt_WL2" "pck_WL2" "tmn_WL2"
## [5] "tmx_WL2" "parent.pop" "elevation.group" "elev_m"
## [9] "Lat" "Long" "cwd" "pck"
## [13] "ppt" "tmn" "tmx" "TimePd"historic_flint_dist <- historic_flint_dist_prep %>%
mutate(ppt_dist=ppt_WL2 - ppt,
cwd_dist=cwd_WL2 - cwd,
pck_dist=pck_WL2 - pck,
tmn_dist=tmn_WL2 - tmn,
tmx_dist=tmx_WL2 - tmx) %>%
dplyr::select(parent.pop, elevation.group, elev_m, ends_with("_dist"))Figures Recent (subtraction distance)
recent_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, cwd_dist), y=cwd_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_MeanCWD_DistfromWL2_RecentClim_wtr_year.png", width = 12, height = 6, units = "in")
recent_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, pck_dist), y=pck_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_MeanPCK_DistfromWL2_RecentClim_wtr_year.png", width = 12, height = 6, units = "in")
recent_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ppt_dist), y=ppt_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_MeanPPT_DistfromWL2_RecentClim_wtr_year.png", width = 12, height = 6, units = "in")
recent_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, tmn_dist), y=tmn_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_MeanTMN_DistfromWL2_RecentClim_wtr_year.png", width = 12, height = 6, units = "in")
recent_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, tmx_dist), y=tmx_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_MeanTMX_DistfromWL2_RecentClim_wtr_year.png", width = 12, height = 6, units = "in")Figures Historical (subtraction distance)
historic_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, cwd_dist), y=cwd_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_MeanCWD_DistfromWL2_HistoricalClim_wtr_year.png", width = 12, height = 6, units = "in")
historic_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, pck_dist), y=pck_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_MeanPCK_DistfromWL2_HistoricalClim_wtr_year.png", width = 12, height = 6, units = "in")
historic_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ppt_dist), y=ppt_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_MeanPPT_DistfromWL2_HistoricalClim_wtr_year.png", width = 12, height = 6, units = "in")
historic_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, tmn_dist), y=tmn_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_MeanTMN_DistfromWL2_HistoricalClim_wtr_year.png", width = 12, height = 6, units = "in")
historic_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, tmx_dist), y=tmx_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_MeanTMX_DistfromWL2_HistoricalClim_wtr_year.png", width = 12, height = 6, units = "in")Bioclim Climate Distance
Gowers
#need to modify the gowers_calc function to only include bioclim variables
gowers_calc_bioclim <- function(data, indices, P) { #function with all of the code necessary for calculating gowers distance
#data = _clim_boot (recent or historical) - needs to be nested by year; P = # climate variables
#need to make WL2_range_prep before running this function
data <-data[indices,] # subset per bootstrap indices
data <- data %>% unnest(data) #unnest so the function can access the climate data
data_means <- data %>%
group_by(parent.pop, elevation.group, elev_m, Lat, Long) %>%
summarise_at(c("ann_tmean", "mean_diurnal_range",
"temp_seasonality", "temp_ann_range",
"tmean_wettest_quarter", "tmean_driest_quarter", "ann_ppt",
"ppt_seasonality","ppt_warmest_quarter", "ppt_coldest_quarter"),
c(mean), na.rm = TRUE) #get 30 year averages for each climate variable
range_merge <- bind_rows(data_means, WL2_range_prep)
WL2_home_climate_ranges <- range_merge %>% #calculate ranges
ungroup() %>%
summarise(ann_tmean_range=max(ann_tmean)-min(ann_tmean),
mean_diurnal_range_range=max(mean_diurnal_range)-min(mean_diurnal_range),
temp_seasonality_range=max(temp_seasonality)-min(temp_seasonality),
temp_ann_range_range=max(temp_ann_range)-min(temp_ann_range),
tmean_wettest_quarter_range=max(tmean_wettest_quarter)-min(tmean_wettest_quarter),
tmean_driest_quarter_range=max(tmean_driest_quarter)-min(tmean_driest_quarter),
ann_ppt_range=max(ann_ppt)-min(ann_ppt),
ppt_seasonality_range=max(ppt_seasonality)-min(ppt_seasonality),
ppt_warmest_quarter_range=max(ppt_warmest_quarter)-min(ppt_warmest_quarter),
ppt_coldest_quarter_range=max(ppt_coldest_quarter)-min(ppt_coldest_quarter))
WL2_home_climate <- bind_cols(WL2_climate_all, data_means) #add WL2 climate data to home climate data
WL2_home_climate_with_ranges <- bind_cols(WL2_home_climate, WL2_home_climate_ranges) #add in ranges
gowers_calc_each_var <- WL2_home_climate_with_ranges %>% #variable by variable calc
mutate(ann_tmean_gowers=abs(ann_tmean_WL2 - ann_tmean) / ann_tmean_range,
mean_diurnal_range_gowers=abs(mean_diurnal_range_WL2 - mean_diurnal_range) / mean_diurnal_range_range,
temp_seasonality_gowers=abs(temp_seasonality_WL2 - temp_seasonality) / temp_seasonality_range,
temp_ann_range_gowers=abs(temp_ann_range_WL2 - temp_ann_range) / temp_ann_range_range,
tmean_wettest_quarter_gowers=abs(tmean_wettest_quarter_WL2 - tmean_wettest_quarter) / tmean_wettest_quarter_range,
tmean_driest_quarter_gowers=abs(tmean_driest_quarter_WL2 - tmean_driest_quarter) / tmean_driest_quarter_range,
ann_ppt_gowers=abs(ann_ppt_WL2 - ann_ppt) / ann_ppt_range,
ppt_seasonality_gowers=abs(ppt_seasonality_WL2 - ppt_seasonality) / ppt_seasonality_range,
ppt_warmest_quarter_gowers=abs(ppt_warmest_quarter_WL2 - ppt_warmest_quarter) / ppt_warmest_quarter_range,
ppt_coldest_quarter_gowers=abs(ppt_coldest_quarter_WL2 - ppt_coldest_quarter) / ppt_coldest_quarter_range) %>%
dplyr::select(parent.pop, elevation.group, elev_m, ends_with("_gowers"))
gowers_calc_per_pop <- gowers_calc_each_var %>% #final gowers calc
mutate(Gowers_Dist=(1/P)*(ann_tmean_gowers + mean_diurnal_range_gowers +
temp_seasonality_gowers +temp_ann_range_gowers +
tmean_wettest_quarter_gowers +
tmean_driest_quarter_gowers +ann_ppt_gowers +
ppt_seasonality_gowers + ppt_warmest_quarter_gowers +
ppt_coldest_quarter_gowers)) %>%
dplyr::select(parent.pop, elevation.group, elev_m, Gowers_Dist)
gowers_calc_per_pop %>% pull(Gowers_Dist) #make the result a vector
}Recent
recent_clim_boot_bioclim_nest <- recent_clim_boot %>% select(parent.pop:year, ann_tmean:ppt_coldest_quarter) %>% nest(.by=year)
gowers.boot_bioclim_recent <- boot(data=recent_clim_boot_bioclim_nest, statistic=gowers_calc_bioclim, R=1000, P=10) #will sample each row (year) with replacement
gowers.boot_bioclim_recent$t0 #looks correct ## [1] 0.6563077 0.5425798 0.3873179 0.3883452 0.4086758 0.5013511 0.5049778
## [8] 0.4393398 0.4455919 0.4477789 0.5736585 0.3989203 0.3932999 0.3838464
## [15] 0.5115852 0.4110205 0.4014730 0.3827429 0.4588038 0.4511887 0.4084294
## [22] 0.4041521 0.4018008#str(gowers.boot_bioclim_recent)
for(i in 1:23) {
plot(gowers.boot_bioclim_recent, index=i) #distributions look normal for the most part
}
#boot.ci(gowers.boot_bioclim_recent, type="norm", index = 1) # for the first pop
boot_bioclim_recent_results <- tidy(gowers.boot_bioclim_recent,conf.int=TRUE,conf.method="norm") %>% #all pops
rename(Gowers_Dist = statistic) %>%
mutate(TimePd="Recent")Historic
historical_clim_boot_bioclim_nest <- historical_clim_boot %>% select(parent.pop:year, ann_tmean:ppt_coldest_quarter) %>% nest(.by=year)
gowers.boot_bioclim_historical <- boot(data=historical_clim_boot_bioclim_nest, statistic=gowers_calc_bioclim, R=1000, P=10) #will sample each row (year) with replacement
gowers.boot_bioclim_historical$t0 #looks correct ## [1] 0.5765702 0.5033452 0.4722676 0.4833526 0.4127201 0.5073120 0.4846540
## [8] 0.5643323 0.5691501 0.5662965 0.5691550 0.4313260 0.4262888 0.4302877
## [15] 0.4863573 0.4555654 0.4613156 0.3960979 0.4957256 0.5806481 0.4282173
## [22] 0.4563510 0.4666147#str(gowers.boot_bioclim_historical)
for(i in 1:23) {
plot(gowers.boot_bioclim_historical, index=i) #distributions look normal for the most part
}
#boot.ci(gowers.boot_bioclim_historical, type="norm", index = 1) # for the first pop
boot_bioclim_historical_results <- tidy(gowers.boot_bioclim_historical,conf.int=TRUE,conf.method="norm") %>% #all pops
rename(Gowers_Dist = statistic) %>%
mutate(TimePd="Historical")Plot the results
boot_bioclim_gowers_recent_pops <- bind_cols(pops, boot_bioclim_recent_results) %>% arrange(Gowers_Dist)
boot_bioclim_gowers_historical_pops <- bind_cols(pops, boot_bioclim_historical_results) %>% arrange(Gowers_Dist)
boot_bioclim_gowers_results_all <- bind_rows(boot_bioclim_gowers_recent_pops, boot_bioclim_gowers_historical_pops)
write_csv(boot_bioclim_gowers_results_all, "../output/Climate/full_year_GowersEnvtalDist_WL2bioclim_wtr_year.csv")
recent_fig <- boot_bioclim_gowers_results_all %>%
filter(TimePd=="Recent") %>%
ggplot(aes(x=fct_reorder(parent.pop, Gowers_Dist), y=Gowers_Dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=conf.low, ymax=conf.high),
width=.1, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(y="Gowers Envtal Distance \n from WL2", fill="Elevation (m)", x="Population", title = "Recent Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
historical_fig <- boot_bioclim_gowers_results_all %>%
filter(TimePd=="Historical") %>%
ggplot(aes(x=fct_reorder(parent.pop, Gowers_Dist), y=Gowers_Dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=conf.low, ymax=conf.high),
width=.1, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(y="Gowers Envtal Distance \n from WL2", fill="Elevation (m)", x="Population", title="Historic Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
plot_grid(historical_fig, recent_fig)
ggsave("../output/Climate/full_year_Gowers_BioClim_fromWL2_wtr_year.png", width = 24, height = 8, units = "in")Subtraction
pops_bioclim_avgs <- read_csv("../output/Climate/fullyear_BioClimAvgs_wtr_year.csv") ## Rows: 46 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): parent.pop, elevation.group, TimePd
## dbl (13): elev_m, Lat, Long, ann_tmean, mean_diurnal_range, temp_seasonality...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#Recent
pops_bioclim_recent_avgs <- pops_bioclim_avgs %>% filter(TimePd=="Recent")
recent_bioclim_dist_prep <- bind_cols(wl2_bioclim_all_year_final, pops_bioclim_recent_avgs)
recent_bioclim_dist <- recent_bioclim_dist_prep %>%
mutate(ann_tmean_dist=ann_tmean_WL2 - ann_tmean,
mean_diurnal_range_dist=mean_diurnal_range_WL2 - mean_diurnal_range,
temp_seasonality_dist=temp_seasonality_WL2 - temp_seasonality,
temp_ann_range_dist=temp_ann_range_WL2 - temp_ann_range,
tmean_wettest_quarter_dist=tmean_wettest_quarter_WL2 - tmean_wettest_quarter,
tmean_driest_quarter_dist=tmean_driest_quarter_WL2 - tmean_driest_quarter,
ann_ppt_dist=ann_ppt_WL2 - ann_ppt,
ppt_seasonality_dist=ppt_seasonality_WL2 - ppt_seasonality,
ppt_warmest_quarter_dist=ppt_warmest_quarter_WL2 - ppt_warmest_quarter,
ppt_coldest_quarter_dist=ppt_coldest_quarter_WL2 - ppt_coldest_quarter) %>%
dplyr::select(parent.pop, elevation.group, elev_m, ends_with("_dist"))
recent_bioclim_dist## parent.pop elevation.group elev_m ann_tmean_dist mean_diurnal_range_dist
## 1 BH Low 511.4294 -9.3131111 -4.333333
## 2 CC Low 313.0000 -9.7247361 -2.944472
## 3 CP2 High 2244.1329 -0.3630556 -1.920000
## 4 CP3 High 2266.3822 0.3462222 -1.806222
## 5 DPR Mid 1018.5919 -7.1314028 -2.052361
## 6 FR Mid 787.0000 -5.9816806 -4.104028
## 7 IH Low 454.1298 -8.5100556 -3.230111
## 8 LV1 High 2593.4166 1.9999306 -2.288083
## 9 LV3 High 2353.5507 1.9939861 -2.309306
## 10 LVTR1 High 2741.3898 2.1510694 -2.398861
## 11 SC Low 421.5178 -8.8104167 -3.606111
## 12 SQ1 Mid 1921.0366 -2.8149167 -1.716444
## 13 SQ2 Mid 1934.4512 -2.6137500 -1.950556
## 14 SQ3 High 2373.1707 -0.4783889 -2.035833
## 15 TM2 Low 379.1527 -9.3726389 -2.712167
## 16 WL1 Mid 1613.8372 -3.8918194 -2.458417
## 17 WL2 High 2020.1158 -1.6352917 -1.624806
## 18 WR Mid 1158.0000 -6.1468194 -1.627139
## 19 WV Mid 748.8571 -5.3123194 -3.906750
## 20 YO11 High 2872.2950 3.0110278 -2.697556
## 21 YO4 High 2157.5739 -1.6302361 -1.571583
## 22 YO7 High 2469.9787 0.9207917 -2.985639
## 23 YO8 High 2590.9784 1.3999861 -3.063750
## temp_seasonality_dist temp_ann_range_dist tmean_wettest_quarter_dist
## 1 60.12698 -2.0040000 -10.1081111
## 2 91.94236 0.1646667 -10.8910000
## 3 71.24699 0.5780000 -2.1239444
## 4 67.72248 0.5523333 -1.4004444
## 5 67.09243 0.5806667 -8.1318333
## 6 47.24314 -2.3043333 -6.7739444
## 7 92.70024 -0.2370000 -9.9497778
## 8 90.18034 0.4240000 0.3278889
## 9 91.93763 0.4423333 0.2443889
## 10 93.58073 0.2493333 0.3952778
## 11 110.93393 -0.1400000 -10.2697222
## 12 62.52196 0.5030000 -4.1563889
## 13 62.54707 0.2413333 -3.9883333
## 14 72.07275 0.2823333 -2.0042778
## 15 87.03036 0.3396667 -10.5082222
## 16 77.62394 0.3550000 -5.6761111
## 17 73.25949 1.0186667 -3.4465556
## 18 61.41065 0.9270000 -7.1178333
## 19 38.67997 -2.2293333 -6.3482222
## 20 68.30997 -0.4120000 0.9432222
## 21 90.47103 1.4803333 -3.0478889
## 22 98.36814 0.1410000 -0.6753889
## 23 94.83636 0.0040000 -0.1516667
## tmean_driest_quarter_dist ann_ppt_dist ppt_seasonality_dist
## 1 -1.9028889 1903.3553 -9.110449
## 2 -2.5522222 1472.4147 -6.947168
## 3 6.6615556 1198.2757 5.864108
## 4 7.3550556 1253.0557 7.199701
## 5 -0.3026667 1029.8414 -3.577777
## 6 1.1562778 1466.9467 -5.870672
## 7 -1.3938333 1413.3157 -4.899665
## 8 9.1186667 729.3860 10.098917
## 9 9.1310000 761.3737 10.577557
## 10 9.3248889 670.8974 10.220148
## 11 -1.8075000 1696.0340 -4.240618
## 12 4.5318333 1544.7580 -4.161716
## 13 4.7060556 1523.8407 -5.101853
## 14 6.8301667 1445.5947 -5.269255
## 15 -2.4013889 1354.4123 -7.083399
## 16 3.2717222 1207.5217 2.999783
## 17 5.4460000 1201.3210 5.603420
## 18 0.6517222 872.5587 -3.315078
## 19 1.5997222 1467.0617 1.637537
## 20 10.3630556 1668.6830 4.871678
## 21 5.6748889 1355.3087 -3.613782
## 22 8.4037222 1298.5924 -2.681381
## 23 8.8713333 1308.5564 -2.574380
## ppt_warmest_quarter_dist ppt_coldest_quarter_dist
## 1 41.3263333 1083.4730
## 2 10.5470000 856.4733
## 3 -39.6546667 758.2577
## 4 -33.7856667 789.9770
## 5 -38.5873333 633.9707
## 6 -5.2143333 860.0967
## 7 -3.4016667 838.0123
## 8 -86.1206667 581.2507
## 9 -83.7860000 601.9627
## 10 -98.2480000 557.9347
## 11 20.5286667 990.6537
## 12 -15.7820000 907.3167
## 13 -8.8176667 894.6653
## 14 -13.3300000 851.5623
## 15 -0.7236667 790.1980
## 16 -31.2276667 758.7450
## 17 -34.7400000 763.0367
## 18 -53.5583333 550.7474
## 19 2.2883333 867.8673
## 20 3.1750000 974.4007
## 21 -7.6973333 800.9363
## 22 -17.6720000 762.1380
## 23 -16.9370000 764.7230#Historical
pops_bioclim_historical_avgs <- pops_bioclim_avgs %>% filter(TimePd=="Historical")
historical_bioclim_dist_prep <- bind_cols(wl2_bioclim_all_year_final, pops_bioclim_historical_avgs)
historical_bioclim_dist <- historical_bioclim_dist_prep %>%
mutate(ann_tmean_dist=ann_tmean_WL2 - ann_tmean,
mean_diurnal_range_dist=mean_diurnal_range_WL2 - mean_diurnal_range,
temp_seasonality_dist=temp_seasonality_WL2 - temp_seasonality,
temp_ann_range_dist=temp_ann_range_WL2 - temp_ann_range,
tmean_wettest_quarter_dist=tmean_wettest_quarter_WL2 - tmean_wettest_quarter,
tmean_driest_quarter_dist=tmean_driest_quarter_WL2 - tmean_driest_quarter,
ann_ppt_dist=ann_ppt_WL2 - ann_ppt,
ppt_seasonality_dist=ppt_seasonality_WL2 - ppt_seasonality,
ppt_warmest_quarter_dist=ppt_warmest_quarter_WL2 - ppt_warmest_quarter,
ppt_coldest_quarter_dist=ppt_coldest_quarter_WL2 - ppt_coldest_quarter) %>%
dplyr::select(parent.pop, elevation.group, elev_m, ends_with("_dist"))
historical_bioclim_dist## parent.pop elevation.group elev_m ann_tmean_dist mean_diurnal_range_dist
## 1 BH Low 511.4294 -8.4101111 -4.880278
## 2 CC Low 313.0000 -8.9929167 -3.708556
## 3 CP2 High 2244.1329 0.8120278 -2.504667
## 4 CP3 High 2266.3822 1.4619306 -2.298917
## 5 DPR Mid 1018.5919 -6.0330417 -3.135806
## 6 FR Mid 787.0000 -5.4016111 -5.236444
## 7 IH Low 454.1298 -7.8325694 -3.820417
## 8 LV1 High 2593.4166 3.5136806 -3.459750
## 9 LV3 High 2353.5507 3.5100139 -3.478472
## 10 LVTR1 High 2741.3898 3.6590139 -3.460028
## 11 SC Low 421.5178 -8.3416528 -4.625250
## 12 SQ1 Mid 1921.0366 -1.3322639 -1.959472
## 13 SQ2 Mid 1934.4512 -1.2701944 -2.017889
## 14 SQ3 High 2373.1707 0.8605139 -1.871972
## 15 TM2 Low 379.1527 -8.5836667 -3.610944
## 16 WL1 Mid 1613.8372 -2.7882917 -3.144528
## 17 WL2 High 2020.1158 -0.5167361 -2.313861
## 18 WR Mid 1158.0000 -4.9652778 -3.020333
## 19 WV Mid 748.8571 -4.4950556 -4.575889
## 20 YO11 High 2872.2950 4.4222361 -4.060361
## 21 YO4 High 2157.5739 -0.4266944 -2.238556
## 22 YO7 High 2469.9787 2.0677639 -3.287417
## 23 YO8 High 2590.9784 2.6075833 -3.601167
## temp_seasonality_dist temp_ann_range_dist tmean_wettest_quarter_dist
## 1 71.56214 -2.29466667 -10.0385000
## 2 87.19835 -0.78933333 -10.3733333
## 3 97.92445 0.50266667 -1.3242222
## 4 96.90821 0.63866667 -0.6765000
## 5 87.12842 0.03233333 -7.4813333
## 6 62.08451 -2.97266667 -6.5857222
## 7 95.93144 -0.63633333 -9.1192778
## 8 112.63054 -0.87633333 0.6493889
## 9 115.07855 -0.85933333 0.6277778
## 10 115.13347 -0.93433333 0.7672778
## 11 102.99453 -1.36366667 -9.8061667
## 12 106.94249 1.44800000 -3.6233889
## 13 108.32565 1.33133333 -3.5983333
## 14 120.98057 1.55933333 -1.5965556
## 15 87.27145 -0.59633333 -10.1672778
## 16 95.71314 -0.14266667 -4.4161667
## 17 100.01427 0.77700000 -2.2460000
## 18 86.14137 0.14100000 -6.3370556
## 19 40.24518 -3.02100000 -5.2930000
## 20 90.11428 -1.17133333 2.0145556
## 21 108.73489 1.14166667 -2.9103889
## 22 113.15101 0.18700000 -0.3375000
## 23 110.30727 -0.16200000 0.2391111
## tmean_driest_quarter_dist ann_ppt_dist ppt_seasonality_dist
## 1 -1.9880000 1905.5137 0.1930321
## 2 -2.0086667 1502.6400 2.1868360
## 3 7.5140556 1160.9090 15.0348448
## 4 8.1563333 1208.0227 16.0775515
## 5 0.6037222 1038.0264 3.2365914
## 6 1.0405000 1474.7540 4.8537233
## 7 -0.8496667 1428.3607 2.6366144
## 8 10.4668333 690.2234 17.0436567
## 9 10.4784444 725.0794 17.3333599
## 10 10.6676667 625.9940 17.1423848
## 11 -1.2587778 1744.1830 4.6997444
## 12 5.4824444 1477.9310 3.3277293
## 13 5.4880556 1450.9007 2.9995796
## 14 7.6558333 1357.9723 3.4632855
## 15 -1.7044444 1384.7190 2.1816968
## 16 3.9617222 1260.7260 10.8706328
## 17 6.2809444 1227.5207 13.9179169
## 18 1.7487222 882.3864 3.7810263
## 19 1.7430556 1471.4467 9.6751948
## 20 11.7016111 1683.5047 15.1374811
## 21 6.1025000 1345.8883 6.2142378
## 22 8.9069444 1267.5780 9.0912423
## 23 9.3500556 1277.7307 9.1648775
## ppt_warmest_quarter_dist ppt_coldest_quarter_dist
## 1 24.273333 1129.7517
## 2 -8.555000 916.2960
## 3 -70.518000 794.9257
## 4 -66.787667 821.6800
## 5 -59.336667 689.8360
## 6 -8.376000 899.0523
## 7 -19.302667 885.5520
## 8 -139.933000 660.7384
## 9 -136.130330 680.3997
## 10 -139.577997 639.7394
## 11 6.138333 1050.8350
## 12 -28.372000 911.6023
## 13 -28.876667 898.9423
## 14 -30.469667 853.5327
## 15 -20.677000 857.5300
## 16 -42.813667 824.7807
## 17 -53.003667 821.4010
## 18 -66.500333 616.8060
## 19 -10.644333 911.1373
## 20 -2.565000 1030.6650
## 21 -27.707000 860.0080
## 22 -35.585333 815.8487
## 23 -34.903333 817.7073Figures Recent (subtraction distance)
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ann_tmean_dist), y=ann_tmean_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_Ann_Tmean_DistfromWL2_RecentClim_wtr_year.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, mean_diurnal_range_dist), y=mean_diurnal_range_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_Diurnal_Range_DistfromWL2_RecentClim_wtr_year.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, temp_seasonality_dist), y=temp_seasonality_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_Temp_Seasonality_DistfromWL2_RecentClim_wtr_year.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, temp_ann_range_dist), y=temp_ann_range_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_Temp_Ann_Range_DistfromWL2_RecentClim_wtr_year.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, tmean_wettest_quarter_dist), y=tmean_wettest_quarter_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_Temp_Wet_DistfromWL2_RecentClim_wtr_year.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, tmean_driest_quarter_dist), y=tmean_driest_quarter_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_Temp_Dry_DistfromWL2_RecentClim_wtr_year.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ann_ppt_dist), y=ann_ppt_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_Ann_PPT_DistfromWL2_RecentClim_wtr_year.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ppt_seasonality_dist), y=ppt_seasonality_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_PPT_Seasonality_DistfromWL2_RecentClim_wtr_year.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ppt_warmest_quarter_dist), y=ppt_warmest_quarter_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_PPT_Warm_DistfromWL2_RecentClim_wtr_year.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ppt_coldest_quarter_dist), y=ppt_coldest_quarter_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_PPT_Cold_DistfromWL2_RecentClim_wtr_year.png", width = 12, height = 6, units = "in")Historical (subtraction distance)
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ann_tmean_dist), y=ann_tmean_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_Ann_Tmean_DistfromWL2_HistoricalClim_wtr_year.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, mean_diurnal_range_dist), y=mean_diurnal_range_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_Diurnal_Range_DistfromWL2_HistoricalClim_wtr_year.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, temp_seasonality_dist), y=temp_seasonality_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_Temp_Seasonality_DistfromWL2_HistoricalClim_wtr_year.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, temp_ann_range_dist), y=temp_ann_range_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_Temp_Ann_Range_DistfromWL2_HistoricalClim_wtr_year.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, tmean_wettest_quarter_dist), y=tmean_wettest_quarter_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_Temp_Wet_DistfromWL2_HistoricalClim_wtr_year.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, tmean_driest_quarter_dist), y=tmean_driest_quarter_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_Temp_Dry_DistfromWL2_HistoricalClim_wtr_year.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ann_ppt_dist), y=ann_ppt_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_Ann_PPT_DistfromWL2_HistoricalClim_wtr_year.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ppt_seasonality_dist), y=ppt_seasonality_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_PPT_Seasonality_DistfromWL2_HistoricalClim_wtr_year.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ppt_warmest_quarter_dist), y=ppt_warmest_quarter_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_PPT_Warm_DistfromWL2_HistoricalClim_wtr_year.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ppt_coldest_quarter_dist), y=ppt_coldest_quarter_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/all-year_PPT_Cold_DistfromWL2_HistoricalClim_wtr_year.png", width = 12, height = 6, units = "in")